Unified artificial intelligence model for multiple customer value variable prediction

ABSTRACT

A unified model for a neural network can be used to predict a particular value, such as a customer value. In various instances, customer value may have particular sub-components. Taking advantage of this fact, a specific learning architecture can be used to predict not just customer value (e.g. a final objective) but also the sub-components of customer value. This allows improved accuracy and reduced error in various embodiments.

TECHNICAL FIELD

This disclosure includes techniques relating to advanced artificialintelligence techniques. Particularly, this disclosure describesmethods, systems, and techniques for densely connected neural networks,multi-variable modeling including sub-variable modeling in a unifiedmodel, and convolutional neural network techniques usable with rawtransactional data.

BACKGROUND

Modern data modeling is a complicated enterprise, especially in big dataenvironments. Particularly in systems with a large number of users, itcan be useful to accurately predict future user behavior and theoutcomes of this behavior. System and enterprise resources can beallocated to better effect if appropriate planning can be done based onmodel predictions.

While existing modeling techniques can be used to predict user behaviorand its outcomes, such models can often be improved. With more effectivemodeling techniques, better predictions of user behavior—particularly intransactional systems—can lead to increased productivity and utilizationof computing resources. Machine learning techniques, for example, arenot always well optimized. Machine learning and artificial intelligenceimprovements can therefore lead to significant results in outcomeswithin transactional or other systems.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a block diagram of a system that includes users'devices, an analysis system, a transaction system, a network, and anevents database according to some embodiments.

FIG. 2 illustrates a block diagram of a set of data records, accordingto some embodiments.

FIG. 3 illustrates a flow diagram of a method that relates to anartificial intelligence network for calculating a value according tosome embodiments.

FIG. 4 is a diagram of an architecture related to densely connectedneural networks according to some embodiments.

FIG. 4B is a flow diagram of a method that relates to densely connectedneural networks according to some embodiments.

FIG. 5 is a diagram of an architecture related to a unified model forpredicting customer value along with sub-components of customer value,according to some embodiments.

FIG. 5B is a flow diagram of a method that relates to the unified modelof FIG. 5, according to some embodiments.

FIG. 6 is a diagram of an architecture related to a structuredconvolutional neural network (SCNN), according to some embodiments.

FIG. 6B is a flow diagram of a method that relates to the SCNN of FIG.6, according to some embodiments.

FIG. 7 is a diagram of a computer readable medium, according to someembodiments.

FIG. 8 is a block diagram of a system, according to some embodiments.

DETAILED DESCRIPTION

The present specification allows for the use of densely connected neuralnetworks featuring output forwarding to multiple network layers. Aunified artificial intelligence model for predicting customer value andmultiple sub-variables is also described. And a convolutional neuralnetwork for variable prediction (such as CV) using raw data is furtherdetailed below. These techniques and structures represent improvementsin the field of machine learning and artificial intelligence, andproduce more robust and accurate models than other prior modelingattempts.

Electronic payment transaction service providers often have large userbases, and each user of the service may act differently. These usersconduct greater or lesser numbers transactions, in different amounts,and of different types (e.g. balance transfers, credit card fundedtransactions, bank automated clearing house (ACH) funded transactions,gift card funded transactions, buying goods or services from a merchant,paying bills, transferring currency or other quantities such as rewardpoints to friends and families, etc.). Because of this varying behavior,different customers of a service provider each may contribute adifferent amount of net profit or net loss to the service provider.

One scheme for determining the value of a given customer involvescalculating four quantities for that customer: cost to a serviceprovider, (other) loss to a service provider, revenue derived by theservice provider from the user sending currency (or other quantities),and revenue derived by the service provider from the user receivingcurrency (or other quantities). Note generally that many examples inthis disclosure focus specifically on monetary currency, but thedisclosure is not limited as such. Other scenarios involvingcryptocurrency, airline reward points, etc., are contemplated and withinthe scope of this disclosure.

Cost to a service provider may include fees paid out by the serviceprovider in order to effectuate a transaction. This can include fees toa credit card network, fees to an acquiring bank, etc. In someinstances, cost to the service provider may also include overhead,regulatory, or other costs. Loss to a service provider may includelosses incurred due to fraud or insufficient funds (e.g., a user makes a$100 ACH transaction but the user's bank later denies the ACH forinsufficient funds—however, if a service provider has already moved thismoney to another account or the money has otherwise left the serviceprovider's system, the service provider may incur a partial or totalloss for this money).

Revenue derived by the service provider from the user receiving currency(which may be referred to as Rev_R) can include revenue paid to theservice provider for a transaction where the user is a receiver. Forexample, user A may transfer $50 to user B using her credit card as afunding instrument. A service provider such as PayPal™ might charge userA 2.9% for this service (a total of $1.45). In this example, Rev_R foruser B would be $1.45—the amount received by PayPal™ for the transactionin which user B was the receiver. Similarly, revenue derived by aservice provider from a user sending currency (which may be referred toas Rev_S) can include revenue paid to the service provider for atransaction where the user is a sender. In the example above, Rev_S foruser A would be $1.45.

Thus, in one scheme, the total value for a customer (which may bereferred to as CV) can be calculated by the formulaCV=Cost+Loss+Rev_R+Rev_S. Using historical data such as past transactiondata, a customer's previous CV can be determined (e.g., from existingdata, the CV for a customer over a previous period of time such as thelast 6 or 12 months can be calculated).

Customer CV can be a useful metric in several contexts. When assessingthe risk of a transaction, for example, customer CV can be used as onefactor in determining whether to approve a transaction. A serviceprovider may see a transaction that appears to be fairly high risk(chance of fraud or NSF), for example. If the customer making thattransaction is a relatively high CV customer, the service provider candecide to accept the higher risk and approve the transaction. If thetransaction is denied, the high CV customer might choose to take his orher business elsewhere—and thus, even if the potential for loss on thetransaction is higher than the service provider would normally accept,it may make sense to take the risk. Conversely, risk thresholds may belowered for a low CV customer. If a particular customer is onlymarginally profitable (or is perhaps even unprofitable), a serviceprovider can adopt stricter risk thresholds. Thus, CV can be a datapoint for risk assessment. Another use of CV is in the area of customerservice, where a high CV customer in need of help could be routed withhigher priority to a customer service agent or other resource.

Predicting future CV for a user can also be valuable. In some cases,future CV could be calculated from past behavior—e.g. if a user had anaverage annual CV of $135 for the last 5 years, he may be likely to havea similar CV for the next 12 month period of the future. Using advancedartificial intelligence techniques, however, future CV can be moreaccurately predicted—and can also be predicted even for users withlittle or no transactional history (e.g. new users). Such advancedtechniques are described below.

Densely connected neural networks may be used in some instances tocalculate CV or another quantity. A unified model architecture (whichmay or may not make use of a densely connected neural network) can alsobe used to predict not only CV, but also sub-components of CV.Additionally, a structured convolutional neural network can be used topredict CV or another quantity using raw data (rather than engineereddata) in various embodiments. A joint unified model is also capable ofcalculating CV and its component values while making use of both adensely connected neural network (that may operate on engineered data)and also a structured convolutional neural network.

This specification includes references to “one embodiment,” “someembodiments,” or “an embodiment.” The appearances of these phrases donot necessarily refer to the same embodiment. Particular features,structures, or characteristics may be combined in any suitable mannerconsistent with this disclosure.

“First,” “Second,” etc. As used herein, these terms are used as labelsfor nouns that they precede, and do not necessarily imply any type ofordering (e.g., spatial, temporal, logical, cardinal, etc.).

Various components may be described or claimed as “configured to”perform a task or tasks. In such contexts, “configured to” is used toconnote structure by indicating that the components include structure(e.g., stored logic) that performs the task or tasks during operation.As such, the component can be said to be configured to perform the taskeven when the component is not currently operational (e.g., is not on).Reciting that a component is “configured to” perform one or more tasksis expressly intended not to invoke 35 U.S.C. § 112(f) for thatcomponent.

Turning to FIG. 1, a block diagram of a system 100 is shown. In thisdiagram, system 100 includes user devices 105, 110, 115, an analysissystem 120, a transaction system 160, a network 150, and an AI(artificial intelligence) model 125. Also depicted is events DB(database) 130. Note that other permutations of this figure arecontemplated (as with all figures). While certain connections are shown(e.g. data link connections) between different components, in variousembodiments, additional connections and/or components may exist that arenot depicted. Further, components may be combined with one other and/orseparated into one or more systems.

User devices 105, 110, and 115 may be any type of computing system.Thus, these devices can be a smartphone, laptop computer, desktopcomputer, tablet computer, etc. As discussed below, user devices such as105, 110, and 115 may engage in various actions, including transactions,using transaction system 160. Analysis system 120 may comprise one ormore computing devices each having a processor and a memory, as maytransaction system 160. Network 150 may comprise all or a portion of theInternet.

In various embodiments, analysis system 120 can take operations relatedto creating and/or using artificial intelligence to assign a clusteridentity to a new event. Note that different aspects of operationsdescribed relative to analysis system 120 (as well as other systemsdescribed herein) can be performed by two or more different computersystems in some embodiments. Analysis system 120 may be controlled by anentity who provides an electronically provided service, which may be anelectronic transaction payment service in some instances (allowing fortransfer of currency or other items).

Transaction system 160 may correspond to an electronic payment servicesuch as that provided by PayPal™. Thus, transaction system 160 may havea variety of associated user accounts allowing users to make paymentselectronically and to receive payments electronically. A user accountmay have a variety of associated funding mechanisms (e.g. a linked bankaccount, a credit card, etc.) and may also maintain a currency balancein the electronic payment account. A number of possible differentfunding sources can be used to provide a source of funds (credit,checking, balance, etc.). User devices 105, 110, and 115 can be used toaccess electronic payment accounts such as those provided by PayPal™.

Events database (DB) 130 includes records of various actions taken byusers of transaction system 160. These records can include any number ofdetails, such as any information related to a transaction or to anaction taken by a user on a web page or an application installed on acomputing device (e.g., the PayPal app on a smartphone). Many or all ofthe records in events database 130 are transaction records includingdetails of a user sending or receiving currency (or some other quantity,such as credit card award points, cryptocurrency, etc.), in variousembodiments.

Artificial intelligence (AI) model 125 is constructed and/or implementedby analysis system 120 in various embodiments. Thus, AI model 125 may beimplemented as one or more data structures and programming logic storedand/or managed by analysis system 120 in a number of differentembodiments. As discussed below, AI model 125 may be used to perform avariety of different operations relating to particular machine learningtechniques.

Turning to FIG. 2, a block diagram is shown of one embodiment of samplerecords 200. This diagram is just one example of some of the types ofdata that can be maintained regarding electronic payment transactionsengaged in by a user, and these records may be contained in eventsdatabase 130.

Information maintained in events database 130, and/or other databases,can be used to calculate costs, other losses, and revenues associatedwith a particular user. Different users of an electronic paymenttransaction service provider may use the service in different ways, forexample, varying by frequency, amounts, and the types of transactionsthey perform. Thus, different users may have different amounts of netprofit (or loss) that they generate for a service provider. One user maygenerate a net profit of $200 in a year for a company, while anotheruser might cause a net loss of $25.

As shown, field 202 includes an event ID. This may be a globally uniqueevent identifier within an enterprise associated with transaction system160. Thus, in one embodiment, the event ID in field 202 includes aunique ID for each of millions of electronic payment transactionsprocessed by a service provider such as PayPal™.

Field 204 includes a unique account ID for a user. Field 206 includes acountry code for the user (e.g., US=United States, CA=Canada, etc.).

Fields 208 and 210 represent an IP address date and a transaction amount(which may be specified in a particular currency such as US Dollars,Great Britain Pounds, etc.). The IP address might be the IP address ofthe user at the time the transaction was conducted, for example. Field211 indicates whether a particular transaction was money sent or moneyreceived (e.g., row 1 for event ID 798744654 shows that the user withaccount ID 1234 sent an amount of $5.48, while row 3 for event ID563454210 shows that the user with account ID 7890 received an amount of$2.00).

Field 212 represents a summary of fee costs associated with atransaction. This may be a total of all fees, for example, that are paidout to other parties by an electronic payment transaction serviceprovider such as PayPal™ to effectuate a transaction. These fees mayinclude interchange fees, card network assessment fees, etc.

Field 214 includes a merchant fee charged, e.g., an amount received byan electronic payment transaction service provider. For example, amerchant may charge a customer $100.00 for an item, but will pay adiscount fee of $3.50 to a service provider for processing the payment.Thus, field 214 represents an example of revenue received from atransaction.

Information such as that contained in fields 212 and 214 can be used tohelp calculate certain quantities for a user, such as cost, loss, Rev_R,and Rev_S. Note that in various embodiments, such information may beobtained or calculated from one or more databases associated withtransaction system 160.

Also, many additional pieces of information may be present in eventsdatabase 130 in various embodiments. An email address associated with anaccount (e.g. which can be used by users to direct an electronic paymentto an account using only that account's associated email address) can belisted. Home address, phone number, and any number of other personaldetails can be listed. A transaction timestamp (e.g. date, time, hour,minute, second) is provided in various embodiments.

Turning now to FIG. 3, a diagram 300 is shown illustrating oneembodiment of an artificial intelligence model for calculating aparticular value. In this case, the value being calculated is customervalue (CV).

In the embodiment of FIG. 3, a three-phase neural network is used tocalculate CV. Unlike the embodiment of FIG. 4, discussed below, thisembodiment is not a densely connected neural network.

As depicted, each of the three phases includes a dense layer, a batchnormalization layer, and a dropout layer (indicated as 1, 2, and 3).Thus, the first phase includes dense layer 310, batch normalizationlayer 315, and dropout layer 320. The second phase includes dense layer325, batch normalization layer 330, and dropout layer 335. The thirdphase includes dense layer 340, batch normalization layer 345, anddropout layer 350. An input layer 305 and output task 355 (for CV) arealso shown.

Input layer 305 may provide a variety of inputs for the model. Theseinputs can include past transactions for an electronic paymenttransaction service such as that provided by PayPal™ or other serviceproviders. During training, inputs may be put into a neural networkmany, many times to boost accuracy and obtain a well-functioning model.

Dense layers 310, 325, and 340 include a plurality of neurons. Eachneuron may perform one or more mathematical operations on its inputs,and produce an output. Batch normalization layers 315, 330, and 345 mayadjust data to fit within particular parameters so that smootherprocessing can be obtained. Dropout layers 320, 335, and 350 may selectcertain data for dropout and/or replacement with randomized data (e.g.noise) to prevent overfitting. Note that various aspects described abovewith respect to FIG. 3 may also apply to other models using neuralnetworks discussed herein.

Turning to FIG. 4, a diagram of one embodiment of a densely connectedneural network 400 is shown. The architecture for this densely connectedneural network is broadly applicable, and need not be used only tocalculate CV. In various embodiments, other quantities can be calculatedfor the model—indeed, this architecture can be used for any appropriatemodeling task.

One of the characteristics of a densely connected neural network, invarious embodiments, is that outputs of each prior layer are forwardedto each and every subsequent layer. This contrasts with other neuralnetwork architectures, such as that of FIG. 3, in which layers maysimply be serially connected to one another (e.g. layer 1 outputs tolayer 2 which outputs to layer 3, though layer 1 does not output tolayer 3 directly). By forwarding inputs to each and every subsequentlayer, data visibility persists at a higher level across the neuralnetwork, in various embodiments, though greater complexity can alsoarise due to greater quantities of inputs being handled at deeper layersin the network.

Note that forwarding output to a deeper layer (i.e. not just animmediately subsequent layer) in a neural network does not necessarilymake a densely connected neural network. As noted above, a feature ofdensely connected neural network in various embodiments is that allpossibly output forwarding is performed—that is, a 6^(th) level layerwould get inputs from at least the five previous layers, for example.

In the embodiment of FIG. 4, there are four components, each including adense layer, a batch normalization layer, and a dropout layer. Asdepicted, these components include dense layers 410, 425, 440, and 455,batch normalization layers 415, 430, 445, and 460, and dropout layers420, 435, 450, and 465.

Input layer 405 gives data to the first component via communication flow406 as shown. Unlike other neural networks, however, input layer 405also gives data via communication flows 407, 408, and 409 to the second,third, and fourth components of the neural network. Similarly, the firstcomponent provides output data to the second, third and fourthcomponents via communication flows 421, 422, and 423. The secondcomponent provides output data to the third and fourth components viacommunication flows 436 and 437. The third component provides outputdata to the fourth component via communication flow 451. Meanwhile thefourth component outputs to output task CV 470.

Neural network 400 can be optimized on an output task, such as CV, usinghistorical data in various embodiments. For example, neural network 400(which is densely connected) can be trained using past transaction datafor users and/or other data (e.g. cost data, fraud loss data, revenuesdata, etc.). Training the model can be performed, in the case of CV, bytaking known user-related data and running it through neural network400. Predictions from the neural network 400 can then be compared toactual data (e.g., the network predicted a CV for $110 for a user over a12-month historical period, while actual CV for that customer was $100,indicating a 10% error). Adjustments can then be made to the differentcomponents of the neural network (e.g., to neurons within dense layer410, 425, etc.) to see if tweaking the model results in better accuracy(e.g. did an adjustment get the network closer to predicting thehistorical actual $100 CV for the customer). This process can berepeated many times, for different customers (with potentially millionsor more trials) to tweak the model to produce good results for a largepopulation. Neuron adjustments can include changing weighting and/ormathematical functions at those neurons to produce different results.E.g., for a layer with 4 neurons, weightings might start at 0.25 each,but be shifted in different trials to other values (e.g. 0.4 for twoneurons and 0.1 for two others, etc., with many different variationsobviously possible). Mathematical functions and/or parameters within thecalculations made by the neurons can also be adjusted in various ways.

Input selection operations can also be performed at dense layers such as410, 425, 440, and 455. This may be particularly useful because in adensely connected neural network, in some instances, successive layersmay receive large numbers of inputs that need to be reducedappropriately for the number of neurons at that layer. Consider, forexample, if dense layers 410, 425, 440, and 455 had four neurons each(with each neuron producing one output). In this example, dense layer455 (the deepest layer shown) would be receiving outputs from twelvedifferent neurons (in addition to input layer 405), from communicationpathways 423, 437, and 451. These outputs can be operationally combinedat dense layer 455 prior to processing by that layer's neurons—forexample, the inputs could be simply and linearly combined (e.g., eachneuron output on pathways 423, 437, and 451 could simply be averagedwith one another). Other operators can also be used, however, such asaddition, subtraction, multiplication, or more complex functions aswell, and weightings can also be varied (e.g. weighting for animmediately preceding layer could be higher than a more distantpreceding layer). Input selection operations can also be tweaked andadjusted as part of the process of training neural network 400 (e.g., asthe model is trained, operations that affect the way inputs are sent todeeper layers in the network can be changed—weightings can be adjusted,a multiplication operation can be turned into a subtraction operation,order of operands and/or operators can be shifted, etc.).

Once neural network 400 is trained, it can then be used to predict CV(or another quantity) for various input data. In some cases, newer usersof a service provider can have their CV predicted even if they havelittle or no transaction history. Input data 405 for neural network 400,for example, can include not just transaction history, but also variousprofile data about a user. Someone who has only recently joined PayPal™,for example, may still provide many pieces of information aboutthemselves, such as mailing address, country of residence, linkedfunding sources (debit or credit card, checking account, etc.), an emailaddress, and device information (e.g. what model of computer orsmartphone the user has, whether they have connected to PayPal.com fromdifferent cities, network information such as IP addresses used tologin, additional hardware device information like screen size and otherfixed and/or changeable aspects of the device, etc.). Such informationcan also be used to train neural network 400, which in somecircumstances can allow prediction of CV even for users who do not havemuch transaction history. (E.g., the neural network could have userswith very little or no history during training, but then optimize basedon known data for those users later—that is, the training could take theuser's data as it existed shortly after sign up, for example, and thenlater compare predicted CV to what that (at the time) new customer didin a following time period such as 3 or 12 months).

Turning now to FIG. 4B, a flowchart of one embodiment of a method 480 isshown. This method relates to neural networks such as that describedabove relative to FIG. 4 (as well as elsewhere herein).

Operations described relative to FIG. 4B may be performed, in variousembodiments, by any suitable computer system and/or combination ofcomputer systems, including analysis system 120 and/or transactionsystem 160. For convenience and ease of explanation, however, operationsdescribed below will simply be discussed relative to analysis system120. Further, various elements of operations discussed below may bemodified, omitted, and/or used in a different manner or different orderthan that indicated. Thus, in some embodiments, analysis system 120 mayperform one or more aspects described below, while transaction system160 (or another system) might perform one or more other aspects.

In operation 482, layers of a neural network are created by analysissystem 120, in some embodiments. This operation may include creatingfirst, second, third, and fourth layers of a neural network, each layerincluding one or more neurons (e.g., such as shown in FIG. 4). Greateror lesser numbers of layers may be created in various embodiments.

In operation 484, a first set of data communication pathways areestablished by analysis system 120, in some embodiments. This operationmay include creating pathways leading directly from the outputs ofneurons of a first layer to second, third, and fourth layers in variousembodiments. Note that the term “directly” is used here to indicate thatthese inputs are not intercepted by another layer of the neural network,e.g., an input from a first layer that is sent to a second layer andthen processed would not be said to be sent “directly” to a third layerif the third layer simply receives the processed output from the secondlayer. Further, the term “directly” in this context also does notpreclude processing of outputs of neurons by, e.g., a batchnormalization component or a dropout component prior to forwarding theoutputs, nor does it preclude performing input selection operations asdiscussed above.

Similarly, in operation 486, a second set of data communication pathwaysare established by analysis system 120, in some embodiments. Thisoperation may include creating pathways leading directly from outputs ofneurons of the second layer to third and fourth layers. In operation488, a third set of data communication pathways are established byanalysis system 120, in some embodiments. This operation includesestablishing communication pathways leading directly from outputs of theneurons of a third layer to a fourth layer.

In operation 490, the neural network is trained by analysis system 120,in some embodiments. Training may include using historical data tooptimize the neural network to predict a particular quantity, such asCV.

In operation 492, the trained neural network is operated to predictvalues, in some embodiments. For example, operation 492 can includepredicting CV for one or more users of an electronic payment transactionservice provider. Note particularly that while various operationsdescribed in method 480 can be performed by systems other than analysissystem 120, operation 492 may often be performed by such a differentsystem. It may be the case, for example, that building and training aneural network model is performed by one system, while another systemuses the completed model to make predictions.

After values such as CV are predicted, those values may also be storedin a database for use. For example, a customer record might be updatedwith a predicted CV value that could then be used for certainoperations, such as risk assessment for a user making an electronicpayment transaction, or for handling a customer service query from auser.

Unified Model for Multiple Customer Value Variable Prediction

Turning to FIG. 5, a diagram is shown of one embodiment of a unifiedmodel 500 for predicting customer value (CV) and component pieces ofcustomer value such as cost, loss, revenue derived from a user sendingmoney (Rev_S), and revenue derived from a user receiving money (Rev_R).This unified model allows simultaneous calculation from the same modelfor not only an overall objective (CV) but also calculations for relatedsub-variables (cost, loss, Rev_S, Rev_R) that are related components ofthe overall objective.

In unified model 500, input is first produced from input layer 505. Thisinput can include transaction data and/or user profile data, forexample. This input data is then put into a first series of neuralnetwork modules—in the embodiment shown, these are DBD modules 510, 515,520, and 525.

In the present example, these DBD modules are organized similarly toFIG. 4, where there is a dense layer, a batch normalization layer, and adropout layer. These modules may also be densely connected as in theexample of FIG. 4. However, a densely connected neural network is notrequired and different architectures can be used in various embodiments.

Once the first series of neural network modules (e.g. 510, 515, 520,525) has finished its calculations, output from that series is thendistributed to a plurality of variable sub-task neural network modules.In this example, the sub-task neural network modules comprise a firstsub-task module including dense layers 530 and 531, a second sub-taskmodule including dense layers 535 and 536, a third sub-task moduleincluding dense layers 540 and 541, and a fourth sub-task moduleincluding dense layers 545 and 546. These modules are each respectivelydesigned to generate outputs for the sub-variables loss, cost, Rev_S,and Rev_R, as indicated by tasks 532, 537, 542, and 547.

In the example given, the first sub-task module has two dense layers 530and 531. Output from the dense layer 530 is forwarded to a final taskneural network module that includes dense layer 550 (for calculatingCV). From a high-level perspective, dense layer 530 can be considered asdoing work both to help calculate loss (532) and to calculate customervalue (555). However, the second layer 531 of the two dense layers isonly used for calculating the variable sub-task loss—hence, output fromdense layer 531 is only used for the loss calculation task 532 (and isnot forwarded to dense layer 550 for calculating CV). The other sub-taskmodules work similarly—cost, Rev_R, and Rev_S are all related componentsub-variables for CV, so work can be performed for calculating thosevariables while at the same time also doing work to calculate theoverall task of CV.

Turning to FIG. 5B, a flowchart of one embodiment of a method 580 isshown. This method relates to a unified model such as that describedabove relative to FIG. 5 (as well as other techniques and structuresdescribed elsewhere herein). Thus, in one embodiment, unified model 500is created as a product of executing method 580.

Operations described relative to FIG. 5B may be performed, in variousembodiments, by any suitable computer system and/or combination ofcomputer systems, including analysis system 120 and/or transactionsystem 160. For convenience and ease of explanation, however, operationsdescribed below will simply be discussed relative to analysis system120. Further, various elements of operations discussed below may bemodified, omitted, and/or used in a different manner or different orderthan that indicated. Thus, in some embodiments, analysis system 120 mayperform one or more aspects described below, while transaction system160 (or another system) might perform one or more other aspects.

In operation 582, a series of two or more neural network modules iscreated by analysis system 120, in some embodiments. In someembodiments, each of these modules includes a dense layer of neurons inwhich each of the neurons is connected to all of the neurons for animmediately preceding neural network module. For example, the neurons ofDBD module 525 may all be connected to the neurons of DBD module 520,which are all connected to the neurons of DBD module 515, which are allconnected to the neurons of DBD module 510. (The neurons of DBD module505 are also connected to inputs from input layer 505, though this inputlayer itself does not have neurons in various embodiments.)

In operation 584, a plurality of variable sub-task neural networkmodules are created by analysis system 120, in some embodiments. Each ofthe variable sub-task neural network modules, in various embodiments, isconnected to an output of the last of the series of two or more neuralnetwork modules (such as DBD module 525), where each of the variablesub-task neural network modules is configured to calculate a separateone of a plurality of component variables for predicted customer value.As noted above, for example, a loss variable sub-task neural networkmodule including dense layers 530 and 531 is connected to the output ofDBD module 525, as are other sub-task neural network modules for cost,Rev_S, and Rev_R.

In operation 586, a final task neural network module is created forcalculating customer value by analysis system 120, in some embodiments.This final task neural network module, in various embodiments, isconnected to the output of the last of the series of two or more neuralnetwork modules and is also connected to an intermediate output fromeach of the plurality of variable sub-task neural network modules. Inthe example of FIG. 5, a final task neural network module includes denselayer 550, which is connected to intermediate outputs from dense layers530, 535, 540, and 545. (Note that in this example, further dense layersthat are closest to the immediate sub-task variable calculations, e.g.layers 531, 536, 541, and 546, are not connected to provide output todense layer 550, as these further dense layers are simply optimized forcalculating loss, cost, Rev_S, and Rev_R.)

In operation 588, a unified model (as created in operations above) istrained by analysis system 120, in some embodiments. Training may be asdescribed above, with various neurons, input selection operations,and/or other adjustments being made to optimize the unified model. Notethat different kinds of optimization can be performed—for example, themodel could be initially optimized for CV and also separately optimizedfor the sub-task variables of loss, cost, Rev_R, and Rev_S. This ispossible because of the separate dense layers 531, 536, 541, and 546 invarious embodiments—these dense layers can be adjusted and tweaked fortheir sub-tasks without affecting the calculations of dense layer 550for CV, as those deeper layers do not provide output to the dense layer550.

Accordingly, a unified model created as a product of method 580 may beconfigured to predict, using particular input data pertaining to aparticular customer, each of a plurality of component variables forpredicted customer value and also separately predict total customervalue for that customer. The trained model, for example, can give notonly a CV prediction for a given customer based on his or hertransaction history data and/or profile data, but can separately predictindividual component variables like cost, loss, etc. Having thesevariables separately calculated can be useful in making certainpredictions. As one example, a marketing and/or fraud team may wish toidentify customer cohorts that have a low CV, but have high Rev_R and/orRev_S and high loss. Targeted actions, offers, and/or targetedcommunications could be sent or performed based on these identifications(or other identifications, in other contexts).

An additional benefit of a unified model as described above is areduction in error rates. If, for example, cost, loss, Rev_R, and Rev_Swere all calculated with their own separate models and then combined toprovide a CV estimate model, their errors would be additive—e.g. theerror from using four separate models is greater than simply using aunified model. The techniques above allow for one unified model tocalculate not just CV but also its components.

In operation 590, the unified model is executed for one or more users byanalysis system 120, in some embodiments. Note particularly that whilevarious operations described in method 580 can be performed by systemsother than analysis system 120, operation 590 may often be performed bysuch a different system. It may be the case, for example, that buildingand/or training a unified neural network model is performed by onesystem, while another system uses the completed model to makepredictions. Reporting data can be generated for predicting CV formultiple customers (along with cost, loss, Rev_R, Rev_S) and can bestored in a database and/or transmitted as reporting metrics.

Structured Convolutional Neural Networks

Techniques described above, in various embodiments, are applied onengineered features rather than raw transaction data and/or profile datafor an electronic service provider. Engineered features, for example,may include human selected aspects of raw data, and may also includeoperations on raw data that result in some data being lost or obscured.

As one simple example of a possible engineered feature, consider atransaction record format that includes (A) IP address for which anelectronic payment transaction is being conducted, (B) user destinationshipping address for an order of something, and (C) type of currencybeing provided by user to pay for transaction (e.g. USD). Thus, thesethree different pieces of information might be present in the raw data.For purposes of modeling, however, this information might be combinedinto an engineered feature called “Country for Transaction.” Rules canbe used reconcile the above data into one single piece of information(e.g., if at least two out of three of IP, shipping address, andcurrency indicate the same country such as the United States, thenassign that country to the engineered feature). Many other examples ofengineered features are possible. By using such engineered features formachine learning, parameters can be simplified—but as noted, the cost ofthis simplification can be loss of access to certain portions of the rawunderlying data, in various embodiments.

Convolutional neural networks (CNNs) are sometimes used in imagerecognition applications, such as identifying whether or not a humanface is in an image. Typically, CNNs are not necessarily used forbusiness data applications. However, as described below, CNNS may beadapted for certain business use cases. Note that these adaptationsrepresent unique ways of structuring and processing data using advancedtechnical algorithms and machine learning techniques, and are not simply“business methods” of routinely processing data, as will be apparentfrom the following discussions.

Turning to FIG. 6, a diagram is shown of one embodiment of a structuredconvolutional neural network (SCNN) 600. In this example, input layer605 is connected to a first convolutional module (comprising convolutionlayers 610 and 615, and average pooling layer 620). The firstconvolutional module is in turn connected to a second convolutionalmodule comprising convolution layers 625 and 630, and average poolinglayer 635. The second convolutional module is in turn connected to afully connected module comprising fully connected layers 640 and 645.The fully connected module is lastly connected to output task 650 (forcustomer value, or CV).

Input layer 605, in this example, may feature raw transaction data froman electronic payment transaction service provider (or another type ofservice provider). This transaction data may, for example, includevalues for many possible different features. For example, there may be20, 40, 80, 100, 200, or any other number (higher or lower) of possiblefeatures. As discussed above, this could include values like IP addressof the user at the time of a transaction, what type of device the userwas logged into (smartphone, desktop, laptop, tablet, etc.), make andmodel of such device, amount of transaction, type of funding instrumentsuch as credit card or account balance or debit card, currency type,home address, home country, phone number for account, availabledemographic data like age or gender of user, identity of another userfrom whom currency was received or transferred to (and if that otheruser was a merchant with an established business, type of the business,type of good purchased, etc.). Many additional values may be present fora transaction in addition to those explicitly mentioned above. Invarious embodiments, raw transaction data is used rather than engineeredfeature data.

Turning to FIG. 6B, a flowchart of one embodiment of a method 680 isshown. This method relates to a structured convolutional neural network(SCNN) model such as that described above relative to FIG. 6 (as well asother techniques and structures described elsewhere herein). Thus, invarious embodiment, SCNN model 600 is created and/or used as a productof performing one or more aspects of method 680.

Operations described relative to FIG. 6B may be performed, in variousembodiments, by any suitable computer system and/or combination ofcomputer systems, including analysis system 120 and/or transactionsystem 160. For convenience and ease of explanation, however, operationsdescribed below will simply be discussed relative to analysis system120. Further, various elements of operations discussed below may bemodified, omitted, and/or used in a different manner or different orderthan that indicated. Thus, in some embodiments, analysis system 120 mayperform one or more aspects described below, while transaction system160 (or another system) might perform one or more other aspects.

In operation 682, a structured convolutional neural network (SCNN) iscreated by analysis system 120, in some embodiments. In one embodiment,this operation includes creating an input layer, a first convolutionalmodule connected to the input layer, a second convolutional moduleconnected to the first convolutional module, and a fully connectedmodule connected to the second convolutional module.

The first convolutional module may comprise a first convolutional layer(e.g. layer 610), a second convolutional layer (e.g. layer 615), andfirst average pooling layer (e.g. layer 620). The second convolutionalmodule may comprise a third convolutional layer (e.g. layer 625), afourth convolutional layer (e.g. layer 630), and a second averagepooling layer (e.g. layer 635). The fully connected module may comprisea first fully connected layer (e.g. layer 640) and a second fullyconnected layer (e.g. layer 645).

Convolutional layers (e.g. 610, 615, 625, 630) may be configured toapply one or more convolutions to the input data. A pattern of limitedsize may be applied to the data, for example, with resulting weightsbeing given to the underlying data. The pattern can then be re-appliedto other areas of the data until most or all of the data has beencompared to the pattern.

Average pooling layers (e.g. 620 and 635) may be configured to performpooling operations on various data produced from convolutional layers.(Note that in some embodiments, techniques other than average poolingmay be used, such as maximum pooling). Pooling may allow for effectivedown-sampling in various embodiments, reducing the size of data beingoperated on.

Fully connected layers (e.g. 640 and 645) may include a plurality ofneurons in each layer that are each connected to all the neurons in theprevious layer (e.g. as discussed above relative to variousembodiments). This fully connected layer can allow for finalcalculations to be performed when optimizing for a quantity such ascustomer value.

In operation 684, raw transaction data comprising a plurality of recordsis accessed by analysis system 120, in some embodiments. The records mayeach have a plurality of fields containing a respective feature valuefrom a set of features. Feature set data, for example, can include anyinformation collected about a transaction from a service provider suchas PayPal™, a third-party intermediary, a merchant, or another party, invarious embodiments. Note of course that such information collectionwill comply with applicable privacy rules and regulations in variousjurisdictions as required, in various embodiments.

In operation 686, the raw transaction data is arranged by analysissystem 120, in some embodiments. The raw transaction data may bearranged such that at least one of the plurality of fields (containingfeature data) is re-ordered relative to other fields to increasesimilarity of raw data between adjacent fields of the plurality offields. By re-ordering the data to obtain greater degrees of similaritybetween adjacent fields, the resulting rearranged data can get betterresults when convolutional neural network techniques are applied. Thearrangement of data can be performed manually or automatically invarious embodiments.

Thus, input layer 505 raw transaction data may be arranged in variousembodiments in such a way as to better enable the usage of a CNN. Asnoted above, CNNs can be used to perform image recognition techniques.CNNs can be advantageous for image recognition application because ofthe way that pixels frequently have a relationship to other neighboringpixels. For example, in a natural scene image, most of the pixelsbelonging to an oak tree may be located immediately adjacent to otherpixels also belonging to the same oak tree. Likewise, for other objectsin the image as well.

Data may be arranged as a one-dimensional vector (e.g. multipletransaction records essentially concatenated together) in someembodiments, or as a two-dimensional array in other embodiments (e.g. asan array that is M transactions long and N transaction features wide).Thus, in various embodiments, a SCNN may include one-dimensional datawith one-dimensional convolutions, two-dimensional data withtwo-dimensional convolutions, or two-dimensional data withthree-dimensional convolutions.

In business use cases, individual items of data may have little or norelationship to neighboring data. Transactional and/or other databasesare frequently organized with no regard being paid to how one type ofrecorded data is similar (or dissimilar) to another type of recordeddata. E.g., for a transaction having 50 different feature values, thesevalues may be arranged in the columns of a database such that the valuein column 1 has little or no relationship to the value in column 2,which may have little or no relationship to the value in column 3.Accordingly, CNN techniques may be fairly ineffective for such data.

Business data can be arranged manually or automatically, however, suchthat adjacent data are made to have greater correlations with oneanother. For example, consider a transaction record that includes auser's postal code (e.g. ZIP code) associated with a credit card, aphone number area code for the user's home phone number, and state orprovince for the user's shipping address for a particular order from amerchant. These data might comprise postal ZIP code 78729, U.S. areacode 512, and Texas as the user's shipping address state. Depending onthe organization of the database, columns for these data may be locatedin various positions nowhere particularly close to one other. However,there may be a high degree of correlation between these data. Thus, forpurposes of an SCNN, input data can be rearranged such that these dataare placed adjacent to one another in fields (e.g. columns) of atransaction record.

When automatically rearranging the transaction data, a set of recordscan be analyzed first to determine correlations between the differentfields of the records. For example, by analyzing different records, itmight be seen that a particular value for field #24 has a predictivevalue of 0.45 for another particular value being present in field #73 ofthe same record. Using this data, a specification for rearranging thedata can be automatically generated in some embodiments.

In operation 688, analysis system 120 trains the SCNN. This training mayinclude repeatedly feeding the raw transaction data into the SCNN, insome embodiments, and compares output of the SCNN to known resultsassociated with the raw transaction data, and makes adjustments to aplurality of weighting parameters for the SCNN based on results of thecomparing.

When considering user transaction data, particularly in big datasystems, the sheer size of the number of records involved can maketraditional neural network techniques extremely complex andcomputationally expensive to deliver an adequate solution (e.g. withfully connected networks M×N parameters may be needed). In aconvolutional technique, however, only a small number of weightedparameters may be used (e.g. w1, w2, w3). Using this smaller number ofparameters can avoid overfitting issues and leads to greater scalabilityin some embodiments. These weighting parameters can be adjusted duringtraining of the SCNN, changing the ultimate output of the network basedon the data that is fed in. When optimal or near-optimal weightingvalues are found, for example, then the trained network can be used onother data for which no historical result is known to make predictionsfor CV or another quantity.

Joint Combined Model

In some embodiments, a densely connected neural network (e.g. as in FIG.4) and a structured convolutional neural network (e.g. as in FIG. 6) canbe used in combined joint model that may achieve even greater accuracyfor predicting CV (or another quantity) in various embodiments. Such ajoint combined model can also operate as in FIG. 5 to predict multiplesub-variables for CV such as loss, cost, Rev_S, and Rev_R. For example,a first set of input data may be fed into a densely connected neuralnetwork model (e.g. as in FIG. 4) which is then trained using that data.Meanwhile, a second set of input data may be fed into an SCNN (e.g. asin FIG. 6), which is then likewise trained. Output from the denselyconnected neural network can then be distributed to five different taskcomponents (e.g. loss, cost, Rev_S, Rev_R, and CV) as in FIG. 5.Meanwhile, output from the SCNN can also be distributed to the same fivedifferent task components. Thus, a first dense layer for calculatingloss may receive one set of inputs from the densely connected neuralnetwork but also receive another set of inputs from the SCNN. Likewise,first dense layers for calculating the other components may receivethese joint inputs. Similar to FIG. 5, intermediate outputs from thefirst dense layers for loss, cost, Rev_R, and Rev_S can be forwarded toa CV calculation component, while second dense layers perform finalstage optimizations for loss, cost, Rev_R, and Rev_S. Thus, the jointcombined model may be able to take advantages of the SCNN and thedensely connected neural network in a single model without increasingerror ranges that might be caused from using multiple models. Accuracymay also be improved.

Computer-Readable Medium

Turning to FIG. 7, a block diagram of one embodiment of acomputer-readable medium 700 is shown. This computer-readable medium maystore instructions corresponding to the operations of any of thepreceding figures and/or any techniques described herein, in variousembodiments. Thus, in one embodiment, instructions corresponding toanalysis system 120 may be stored on computer-readable medium 700.

Note that more generally, program instructions may be stored on anon-volatile medium such as a hard disk or FLASH drive, or may be storedin any other volatile or non-volatile memory medium or device as is wellknown, such as a ROM or RAM, or provided on any media capable of staringprogram code, such as a compact disk (CD) medium, DVD medium,holographic storage, networked storage, etc. Additionally, program code,or portions thereof, may be transmitted and downloaded from a softwaresource, e.g., over the Internet, or from another server, as is wellknown, or transmitted over any other conventional network connection asis well known (e.g., extranet, VPN, LAN, etc.) using any communicationmedium and protocols (e.g., TCP/IP, HTTP, HTTPS, Ethernet, etc.) as arewell known. It will also be appreciated that computer code forimplementing aspects of the present invention can be implemented in anyprogramming language that can be executed on a server or server systemsuch as, for example, in C, C+, HTML, Java, JavaScript, Python, or anyother scripting language, such as VBScript. Note that as used herein,the term “computer-readable medium” refers to a non-transitory computerreadable medium.

Computer System

In FIG. 8, one embodiment of a computer system 800 is illustrated.Various embodiments of this system may be analysis system 120,transaction system 160, or any other computer system as discussed aboveand herein.

In the illustrated embodiment, system 800 includes at least one instanceof an integrated circuit (processor) 810 coupled to an external memory815. The external memory 815 may form a main memory subsystem in oneembodiment. The integrated circuit 810 is coupled to one or moreperipherals 820 and the external memory 815. A power supply 805 is alsoprovided which supplies one or more supply voltages to the integratedcircuit 810 as well as one or more supply voltages to the memory 815and/or the peripherals 820. In some embodiments, more than one instanceof the integrated circuit 810 may be included (and more than oneexternal memory 815 may be included as well).

The memory 815 may be any type of memory, such as dynamic random accessmemory (DRAM), synchronous DRAM (SDRAM), double data rate (DDR, DDR2,DDR6, etc.) SDRAM (including mobile versions of the SDRAMs such asmDDR6, etc., and/or low power versions of the SDRAMs such as LPDDR2,etc.), RAMBUS DRAM (RDRAM), static RAM (SRAM), etc. One or more memorydevices may be coupled onto a circuit board to form memory modules suchas single inline memory modules (SIMMs), dual inline memory modules(DIMMs), etc. Alternatively, the devices may be mounted with anintegrated circuit 810 in a chip-on-chip configuration, apackage-on-package configuration, or a multi-chip module configuration.

The peripherals 820 may include any desired circuitry, depending on thetype of system 800. For example, in one embodiment, the system 800 maybe a mobile device (e.g. personal digital assistant (PDA), smart phone,etc.) and the peripherals 820 may include devices for various types ofwireless communication, such as wife, Bluetooth, cellular, globalpositioning system, etc. Peripherals 820 may include one or more networkaccess cards. The peripherals 820 may also include additional storage,including RAM storage, solid state storage, or disk storage. Theperipherals 820 may include user interface devices such as a displayscreen, including touch display screens or multitouch display screens,keyboard or other input devices, microphones, speakers, etc. In otherembodiments, the system 800 may be any type of computing system (e.g.desktop personal computer, server, laptop, workstation, net top etc.).Peripherals 820 may thus include any networking or communication devicesnecessary to interface two computer systems.

Although specific embodiments have been described above, theseembodiments are not intended to limit the scope of the presentdisclosure, even where only a single embodiment is described withrespect to a particular feature. Examples of features provided in thedisclosure are intended to be illustrative rather than restrictiveunless stated otherwise. The above description is intended to cover suchalternatives, modifications, and equivalents as would be apparent to aperson skilled in the art having the benefit of this disclosure.

The scope of the present disclosure includes any feature or combinationof features disclosed herein (either explicitly or implicitly), or anygeneralization thereof, whether or not it mitigates any or all of theproblems addressed by various described embodiments. Accordingly, newclaims may be formulated during prosecution of this application (or anapplication claiming priority thereto) to any such combination offeatures. In particular, with reference to the appended claims, featuresfrom dependent claims may be combined with those of the independentclaims and features from respective independent claims may be combinedin any appropriate manner and not merely in the specific combinationsenumerated in the appended claims.

What is claimed is:
 1. A method for creating a unified model forpredicting customer value using artificial intelligence, comprising:creating, at a computer system, a series of two or more neural networkmodules, each of the neural network modules including a dense layer ofneurons connected to all of the neurons for the immediately precedingneural network module; creating, at the computer system, a plurality ofvariable sub-task neural network modules, wherein each of the variablesub-task neural network modules is connected to an output of the last ofthe series of two or more neural network modules, and wherein each ofthe variable sub-task neural network modules is configured to calculatea separate one of a plurality of component variables for predictedcustomer value; and creating, at the computer system, a final taskneural network module for calculating predicted customer value, whereinthe final task neural network module is connected to the output of thelast of the series of two or more neural network modules and is alsoconnected to an intermediate output from each of the plurality ofvariable sub-task neural network modules; wherein the unified model isconfigured to predict, using particular input data pertaining to aparticular customer, each of the plurality of component variables forpredicted customer value and also separately predict total customervalue for that customer.
 2. The method of claim 1, wherein the unifiedmodel further comprises an input layer connected to one or more of thetwo or more neural network modules.
 3. The method of claim 1, whereinthe two or more neural network modules each includes a normalizationcomponent and a dropout component.
 4. The method of claim 1, whereineach of the plurality of variable sub-task neural network modulescomprises: a first dense layer connected to the output of the last ofthe series of two or more neural network modules; a second dense layerconnected to the output of the first dense layer; and a final sub-taskoutput layer connected to the output of the second dense layer.
 5. Themethod of claim 1, further comprising: training the unified model usinghistorical customer data for a plurality of customers.
 6. The method ofclaim 5, wherein the history customer data includes records oftransactions conducted by the plurality of customers using an electronicpayment transactions service, and wherein the transactions includecredit card transactions, debit card transactions, automated clearinghouse (ACH) transactions, and account balance transactions.
 7. Themethod of claim 1, wherein the plurality of component variables forpredicted customer value consist of: loss for a given customer; cost forthe given customer; revenue derived from the given customer sendingmoney; and revenue derived from the given customer receiving money. 8.The method of claim 1, further comprising: predicting, by the computersystem, total customer values for each of a plurality of different usersof an electronic transaction payment service over a particular period oftime; and generating reporting data regarding the total customer values.9. The method of claim 1, wherein the plurality of component variablesfor predicted customer value comprises loss for a given customer,including predicted losses from fraud.
 10. The method of claim 1,wherein the plurality of component variables for predicted customervalue comprises cost for a given customer, including predicted costscharged to an electronic payment transaction service provider byfinancial institutions from which money is received and/or to whichmoney is sent.
 11. A non-transitory computer-readable medium havingstored thereon instructions that are executable by a computer system tocause the computer system to perform operations comprising: creating aseries of two or more neural network modules, each of the neural networkmodules including a dense layer of neurons connected to all of theneurons for the immediately preceding neural network module; creating aplurality of variable sub-task neural network modules, wherein each ofthe variable sub-task neural network modules is connected to an outputof the last of the series of two or more neural network modules, andwherein each of the variable sub-task neural network modules isconfigured to calculate a separate one of a plurality of componentvariables for predicted customer value; and creating a final task neuralnetwork module for calculating predicted customer value, wherein thefinal task neural network module is connected to the output of the lastof the series of two or more neural network modules and is alsoconnected to an intermediate output from each of the plurality ofvariable sub-task neural network modules; wherein the unified model isconfigured to predict, using particular input data pertaining to aparticular customer, each of the plurality of component variables forpredicted customer value and also separately predict total customervalue for that customer.
 12. The non-transitory computer-readable mediumof claim 11, wherein the unified model further comprises an input layerconnected to one or more of the two or more neural network modules. 13.The non-transitory computer-readable medium of claim 11, wherein the twoor more neural network modules each includes a normalization componentand a dropout component.
 14. The non-transitory computer-readable mediumof claim 11, wherein each of the plurality of variable sub-task neuralnetwork modules comprises: a first dense layer connected to the outputof the last of the series of two or more neural network modules; asecond dense layer connected to the output of the first dense layer; anda final sub-task output layer connected to the output of the seconddense layer.
 15. The non-transitory computer-readable medium of claim11, wherein the plurality of component variables for predicted customervalue consist of: loss for a given customer; cost for the givencustomer; revenue derived from the given customer sending money; andrevenue derived from the given customer receiving money.
 16. Thenon-transitory computer-readable medium of claim 11, wherein theoperations further comprise: predicting total customer values for eachof a plurality of different users of an electronic transaction paymentservice over a particular period of time; and generating reporting dataregarding the total customer values.
 17. A computer system, comprising:a processor; and one or more computer-readable media having storedthereon instructions that are executable to cause the computer system toperform operations comprising: creating a series of two or more neuralnetwork modules, each of the neural network modules including a denselayer of neurons connected to all of the neurons for the immediatelypreceding neural network module; creating a plurality of variablesub-task neural network modules, wherein each of the variable sub-taskneural network modules is connected to an output of the last of theseries of two or more neural network modules, and wherein each of thevariable sub-task neural network modules is configured to calculate aseparate one of a plurality of component variables for predictedcustomer value; and creating a final task neural network module forcalculating predicted customer value, wherein the final task neuralnetwork module is connected to the output of the last of the series oftwo or more neural network modules and is also connected to anintermediate output from each of the plurality of variable sub-taskneural network modules; wherein the unified model is configured topredict, using particular input data pertaining to a particularcustomer, each of the plurality of component variables for predictedcustomer value and also separately predict total customer value for thatcustomer.
 18. The computer system of claim 17, wherein the unified modelfurther comprises an input layer connected to one or more of the two ormore neural network modules.
 19. The computer system of claim 17,wherein the two or more neural network modules each includes anormalization component and a dropout component.
 20. The computer systemof claim 17, wherein each of the plurality of variable sub-task neuralnetwork modules comprises: a first dense layer connected to the outputof the last of the series of two or more neural network modules; asecond dense layer connected to the output of the first dense layer; anda final sub-task output layer connected to the output of the seconddense layer.